Mixed Type Multi-attribute Pairwise Comparisons Learning

Abstract

Building a proactive and unobtrusive recom- mender system is still a challenging task. In the real world, buyers may be offered a lot of choices while trying to choose the item that best suits their preference. Such items may have many attributes, which can complicate the process. The classic approach in decision support systems – to put weights on the importance of each attribute – is not always helpful here. For instance, there are cases when users find it is hard to formulate their priorities explicitly. In this paper, we promote the use of pairwise comparisons, which allow the user preferences to be inferred rather than spell out. Our system aims to learn from a limited number of examples and using clustering to guide the selection of pairs for annotation. The approach is demonstrated in the case of purchasing a used car using a large, real-world data set.